View
220
Download
0
Category
Preview:
Citation preview
The Cryosphere, 9, 1373–1383, 2015
www.the-cryosphere.net/9/1373/2015/
doi:10.5194/tc-9-1373-2015
© Author(s) 2015. CC Attribution 3.0 License.
Comparison between observed and simulated aeolian snow mass
fluxes in Adélie Land, East Antarctica
C. Amory1,2,3,4, A. Trouvilliez1,2,3,4,5, H. Gallée1,2, V. Favier1,2, F. Naaim-Bouvet3,4, C. Genthon1,2, C. Agosta6,
L. Piard1,2, and H. Bellot3,4
1University of Grenoble Alpes, LGGE, 38041 Grenoble, France2CNRS, LGGE, UMR5183, 38401 Grenoble, France3University of Grenoble Alpes, IRSTEA, 38041 Grenoble, France4IRSTEA, UR ETNA, 38402 Saint-Martin d’Hères, France5Cerema-DTecEMF, 29280 Plouzané, France6University of Liège, Department of Geography, 4020 Liège, Belgium
Correspondence to: C. Amory (amory@lgge.obs.ujf-grenoble.fr)
Received: 29 September 2014 – Published in The Cryosphere Discuss.: 5 December 2014
Revised: 28 April 2015 – Accepted: 5 June 2015 – Published: 30 July 2015
Abstract. Using the original setup described in Gallée et al.
(2013), the MAR regional climate model including a cou-
pled snowpack/aeolian snow transport parameterization, was
run at a fine spatial (5 km horizontal and 2 m vertical) resolu-
tion over 1 summer month in coastal Adélie Land. Different
types of feedback were taken into account in MAR includ-
ing drag partitioning caused by surface roughness elements.
Model outputs are compared with observations made at two
coastal locations, D17 and D47, situated respectively 10 and
100 km inland. Wind speed was correctly simulated with pos-
itive values of the Nash test (0.60 for D17 and 0.37 for D47)
but wind velocities above 10 m s−1 were underestimated at
both D17 and D47; at D47, the model consistently under-
estimated wind velocity by 2 m s−1. Aeolian snow transport
events were correctly reproduced with the right timing and a
good temporal resolution at both locations except when the
maximum particle height was less than 1 m. The threshold
friction velocity, evaluated only at D17 for a 7-day period
without snowfall, was overestimated. The simulated aeolian
snow mass fluxes between 0 and 2 m at D47 displayed the
same variations but were underestimated compared to the
second-generation FlowCapt™ values, as was the simulated
relative humidity at 2 m above the surface. As a result, MAR
underestimated the total aeolian horizontal snow transport
for the first 2 m above the ground by a factor of 10 com-
pared to estimations by the second-generation FlowCapt™.
The simulation was significantly improved at D47 if a 1-
order decrease in the magnitude of z0 was accounted for, but
agreement with observations was reduced at D17. Our results
suggest that z0 may vary regionally depending on snowpack
properties, which are involved in different types of feedback
between aeolian transport of snow and z0.
1 Introduction
Measurements of aeolian snow mass fluxes in Antarctica re-
vealed that a large amount of snow is transported by the
wind (Budd, 1966; Wendler, 1989; Mann et al., 2000; Trou-
villiez et al., 2014). The aeolian transport of snow is prob-
ably a significant component of the surface mass balance
distribution over the Antarctic ice sheet. Although estimates
have been proposed based on remote sensing data (Das et al.,
2013), reliable quantifications of the contribution of aeolian
snow transport processes to the Antarctic surface mass bal-
ance (ASMB) can only be assessed by modeling. Previous
estimates using numerical models suggest that erosion and
blowing snow sublimation represent around 10 % of the net
ASMB (Déry and Yau, 2002; Lenaerts et al., 2012a). How-
ever, these evaluations were made without considering the
complex feedback system between snow surface properties,
windborne snow particles, and atmospheric conditions. In-
deed, aeolian erosion promotes the formation of snow surface
structures such as sastrugi, barchans, dunes, and megadunes,
Published by Copernicus Publications on behalf of the European Geosciences Union.
1374 C. Amory et al.: Observed and simulated aeolian snow mass fluxes
which, in turn, alter the atmospheric dynamics (Frezzotti et
al., 2004). Rougher surfaces reduce the wind speed and the
resulting wind-driven erosion of snow (Kodama et al., 1985),
but increase turbulence in the near-surface airflow, thereby
further increasing the aeolian snow mass flux (Frezzotti et
al., 2002). Moreover, the presence of airborne snow parti-
cles and their subsequent sublimation are both responsible
for an increase in air density, which may reduce turbulence in
the surface boundary layer and contribute negatively to snow
erosion (Bintanja, 2000; Wamser and Lykossov, 1995). On
the other hand, the increase in air density strengthens kata-
batic flows (Gallée, 1998). An overview of the different types
of feedback caused by blowing and drifting snow is given in
Gallée et al. (2013).
As previously highlighted (Gallée et al., 2001; Lenaerts
et al., 2012b), there are few reliable data sets on aeolian
snow transport covering a long period with an hourly tem-
poral resolution, making it difficult to evaluate modeling in
Antarctica. One-dimensional (1-D) numerical models have
been compared with aeolian snow transport rates in ideal
cases (Xiao et al., 2000) and with observations (Lenaerts
et al., 2010). Regional climate models have been evaluated
against surface mass balance estimates derived from stake
networks (Gallée et al., 2005; Lenaerts et al., 2012c). The lat-
ter is an integrative method that includes all the components
of the surface mass balance: precipitation, run-off, surface
and windborne snow sublimation, and erosion/deposition of
snow. Aeolian snow transport events simulated by regional
climate models have been compared with remote sensing
techniques (see Palm et al., 2011), and with visual obser-
vations at different polar stations (Lenaerts et al., 2012b) or
with particle impact sensors (Lenaerts et al., 2012c). Aeo-
lian snow mass flux measurements are even rarer. Lenaerts
et al. (2012b) were only able to evaluate their simulations
against annual transport rate values estimated at Terra Nova
Bay by the first version of an acoustic sensor FlowCapt™
(Scarchilli et al., 2010), which overestimated aeolian snow
mass flux (Trouvilliez et al., 2015), and against an extrapo-
lation of optical particle counter sensor measurements per-
formed at Halley (Mann et al., 2000). To improve analyses,
model evaluations thus require more detailed and reliable ae-
olian snow transport measurements in Antarctica.
Here, we present a detailed comparison between outputs
of the regional atmospheric model MAR and data collected
during an aeolian snow transport observation campaign in
Adélie Land, Antarctica (Trouvilliez et al., 2014). We focus
on a 1-month period, (January 2011) during which the ob-
servers were in the field and could visually confirm the oc-
currence of meteorological events. MAR was already evalu-
ated over coastal Adélie Land in terms of the occurrence and
qualitative intensity of aeolian snow transport events in Jan-
uary 2010 (Gallée et al., 2013). However, model outputs were
only compared with a single point of aeolian snow trans-
port measurements using first-generation FlowCapt™ instru-
ments. These sensors are good at detecting aeolian snow
Figure 1. Integrative domain of the MAR in Adélie Land, East
Antarctica. The crosses mark the location of the French Dumont
d’Urville base (DDU) and the two automatic weather and snow sta-
tions used in this study (D17 and D47).
transport events but fail to estimate aeolian snow mass fluxes
(Cierco et al., 2007; Naaim-Bouvet et al., 2010; Trouvilliez et
al., 2015). Second-generation FlowCapt™ instruments were
installed at two new locations in February 2010. Unlike its
first-generation counterpart, the second-generation sensor is
able to provide a lower bound estimate of the aeolian snow
mass fluxes (Trouvilliez et al., 2015). It thus allows compar-
isons not only between the simulated and observed timing
of aeolian snow transport events, but also between the sim-
ulated and observed aeolian snow mass fluxes, which was
previously not the case.
2 Field data
Observations were performed in Adélie Land, East Antarc-
tica (Fig. 1), where surface atmospheric conditions are well
monitored at the permanent French Dumont d’Urville sta-
tion (Favier et al., 2011). The coastal region is character-
ized by frequent strong katabatic winds starting at the break
in slope located approximately 250 km inland (Parish and
Wendler, 1991; Wendler et al., 1997). These katabatic winds
are regularly associated with aeolian snow transport events
(Prud’homme and Valtat, 1957; Trouvilliez et al., 2014),
making Adélie Land an excellent location for observations
of aeolian snow transport. Furthermore, a 40-year accumu-
lation data set is available for Adélie Land and long-term
stake measurements are still made along a 150 km stake line
(Agosta et al., 2012) and in erosion areas (Genthon et al.,
The Cryosphere, 9, 1373–1383, 2015 www.the-cryosphere.net/9/1373/2015/
C. Amory et al.: Observed and simulated aeolian snow mass fluxes 1375
2007; Favier et al., 2011). These data sets give access to the
annual SMB in the area.
Several meteorological campaigns including aeolian snow
transport measurements have already been carried out in
Adélie Land using mechanical traps (Madigan, 1929; Gar-
cia, 1960; Lorius, 1962) and optical particle counter sen-
sors (Wendler, 1989). However, none of the measurements in
Adélie Land or elsewhere in Antarctica fulfill all the require-
ments of an in-depth evaluation of regional climate models.
In 2009, a new aeolian snow transport observation campaign
started in Adélie Land, which was specially designed to op-
timally evaluate models as well as possible, given the pre-
vailing logistical difficulties and limitations (Trouvilliez et
al., 2014). In this context, automatic weather stations (AWS)
that measure wind speed, wind direction, temperature, rela-
tive humidity and snow height at 10 s intervals were installed
at three different locations from the coastline to 100 km in-
land (Trouvilliez et al., 2014). Half-hourly mean values are
stored on a Campbell datalogger at each station. The AWS
are equipped with FlowCapt™ acoustic sensors designed
to quantify the aeolian snow mass fluxes and to withstand
the harsh polar environment. The combination of an AWS
and FlowCapt™ sensors is hereafter referred to as an auto-
matic weather and snow station (AWSS). Two generations
of FlowCapt™ exist and have been evaluated in the French
Alps and in Antarctica (Trouvilliez et al., 2015). Both gener-
ations appear to be good detectors of aeolian snow transport
events. The first-generation instrument failed to correctly es-
timate the snow mass flux with the constructor’s calibration
and even with a new calibration, but the second-generation
instrument is capable of providing a lower bound estimate of
the snow mass flux and a consistent relationship of the flux
versus wind speed.
At each AWSS, FlowCapt™ sensors were set up vertically.
When the lower extremity of the sensor is close to the ground
or is partially buried, the FlowCapt™ is able to detect the
onset of an aeolian snow transport event (i.e., initiation of
saltation). Although the level of the snowpack changes over
the course of the year due to accumulation and ablation pro-
cesses, the sensor can nevertheless record continuous obser-
vations, which is an advantage over single point measure-
ment devices. The FlowCapt™ has better temporal resolution
than visual observations, which are usually made at 6 h inter-
vals. Moreover, the ability of these sensors to detect events
of small magnitude is particularly useful, as satellite mea-
surements can only detect blowing snow events in which the
snow particles are lifted 20 m or more off the surface in the
absence of clouds (Palm et al., 2011). Trouvilliez et al. (2014)
reported that aeolian snow transport events with a maximum
particle height < 4.5 m above ground level (a.g.l.) accounted
for 17 % of the total aeolian snow transport events in the pe-
riod 2010–2011 at D17 coastal site (Table 1). Ground and
satellite observations are thus complementary.
In early 2010, two AWSS equipped with second-
generation FlowCapt™ sensors (2G-FlowCapt™) were set up
Figure 2. Left: the D17 7 m mast with one second-generation
FlowCapt™ sensor. Right: the D47 automatic weather and snow sta-
tion with two second-generation FlowCapt™ sensors.
at sites D17 and D47 (Table 1). Because D47 is located in
a dry snow zone roughly 100 km inland from D17, the two
stations document distinct climatic conditions. At D17, one
2G-FlowCapt™ was mounted from 0 to 1 m a.g.l. on a 7 m
high mast with six levels of cup anemometers and thermo
hygrometers, while at D47 a 1 measurement-level AWS was
equipped with two 2G-FlowCapt™ installed from 0 to 1 and
from 1 to 2 m a.g.l. (Fig. 2). Like the other meteorologi-
cal variables, the half-hourly mean aeolian snow mass flux
recorded by each 2G-FlowCapt™ is stored in the datalogger.
An ultrasonic gauge was installed at D47 to monitor surface
variations, from which the elevation of sensors above the sur-
face is assessed throughout the year. A detailed description
of the equipment at both AWSS can be found in Trouvilliez
et al. (2014). Since we focus on the simulated and observed
snow mass fluxes, our evaluation is limited to the two stations
equipped with 2G-FlowCapt™, i.e., D17 and D47.
3 The MAR model
3.1 General description
MAR is a coupled atmosphere/snowpack/aeolian snow trans-
port regional climate model. Atmospheric dynamics is based
on the hydrostatic approximation of the primitive equations
using the terrain following normalized pressure as a vertical
coordinate to account for topography (Gallée and Schayes,
1994). An explicit cloud microphysical scheme describes ex-
changes between water vapor, cloud droplets, cloud ice crys-
tals (concentration and number), rain drops and snow parti-
cles (Gallée, 1995). The original snowpack and aeolian snow
transport sub-models are described in Gallée et al. (2001).
An improved version is detailed in Gallée et al. (2013) and is
used here.
Eroded snow particles drift from the ground into the atmo-
sphere, and the airborne snow particles are advected from
one horizontal grid cell to the next one downwind. More
generally, airborne snow particles are modeled according to
www.the-cryosphere.net/9/1373/2015/ The Cryosphere, 9, 1373–1383, 2015
1376 C. Amory et al.: Observed and simulated aeolian snow mass fluxes
Table 1. Location and characteristics of the two automatic weather and snow stations (AWSS) used in the present study.
D17 D47
Location 66.7◦ S, 139.9◦ E 67.4◦ S, 138.7◦ E
Altitude 450 m a.s.l. 1560 m a.s.l.
Distance from coast 10 km 110 km
Period of observation Since February 2010 January 2010–December 2012
Atmospheric measurements Wind speed, temperature, and hygrometry at six levels Wind speed, temperature, and hygrometry at 2 m
Aeolian transport measurements Second-generation FlowCapt™ from 0 to 1 m Second-generation FlowCapt™ from 0 to 1 and 1 to 2 m
the microphysical scheme. In particular, the sublimation of
windborne snow particles is a function of air relative hu-
midity. Many different types of feedback that are an inte-
gral part of aeolian transport of snow are included in MAR.
The parameterization of turbulence in the surface bound-
ary layer (SBL) is based on the Monin–Obukhov similar-
ity theory (MO-theory) and accounts for the stabilizing ef-
fect of blowing snow particles, as proposed by Wamser and
Lykossov (1995). Turbulence above the SBL is parameter-
ized using the localE–ε scheme, which consists of two prog-
nostic equations, one for turbulent kinetic energy and the
other for its dissipation (Duynkerke, 1988), and includes a
parameterization of the turbulent transport of snow particles
consistent with classical parameterizations of their sedimen-
tation velocity (Bintanja, 2000). Blowing snow-induced sub-
limation is computed by the microphysical scheme and in-
fluences the heat and moisture budgets in the layers that con-
tain airborne snow particles. Their influence on the radiative
transfer through changes in the atmospheric optical depth is
taken into account (see Gallée and Gorodetskaya, 2010).
Under near-neutral atmospheric conditions, the MO-
theory predicts that the vertical profile of the wind speed
within the SBL is semi-logarithmic:
u(z)=u∗
κln(
z
z0
), (1)
where u(z) is the wind speed at height z, κ = 0.4 is the von
Kármán constant, z0 is the roughness length for momen-
tum and u∗ is the friction velocity that describes the shear
stress exerted by the wind on the surface. Aeolian transport
of snow begins when u∗ exceeds the force required for aero-
dynamic entrainment of snow surface particles, known as
threshold friction velocity (u∗t), which depends on the sur-
face properties of the snow (Gallée et al., 2001). In MAR,
surface processes are modeled using the “soil–ice–snow–
vegetation–atmosphere transfer” scheme (SISVAT; De Rid-
der and Gallée, 1998; Gallée et al., 2001; Lefebre at al.,
2005; Fettweis et al., 2005). The threshold friction velocity
for a smooth surface (u∗tS) depends on dendricity, sphericity,
and grain size for snow density below 330 kg m−3 (see Guy-
omarc’h and Mérindol, 1998), and on snow density alone
above 330 kg m−3. To account for drag partitioning caused
by roughness elements, the threshold friction velocity for
a rough surface (u∗tR) is calculated as in Marticorena and
Bergametti (1995):
u∗tR =u∗tS
Rf
, (2)
where both threshold friction velocities are expressed in
m s−1 and Rf is a ratio factor defined as
Rf = 1−ln(
z0R
z0S)
ln[0.35( 10
z0S)0.8
] , (3)
where z0R and z0S are the surface roughness lengths in meters
for rough and smooth surfaces, respectively. Over smooth
snow surfaces, the roughness length is generally around
10−5–10−4 m (Leonard et al., 2011). In MAR, this value is
set to 5× 10−5 m. In addition to the drag partition, mov-
ing particles in the saltation layer transfer momentum from
the airflow to the surface. Above the saltation layer, the net
effect is similar to that of a stationary roughness element
(Owen, 1964). Thus, saltation leads to an increase in rough-
ness length compared with a situation without windborne
snow, even in the case of a smooth surface. The contribu-
tion of blowing snow particles to the roughness length z0S
is calibrated using Byrd project measurements (Budd et al.,
1966; Gallée et al., 2001):
z0S = 5× 10−5+max
(0.5× 10−6,au2
∗− b), (4)
where a and b are two constants.
One of the main surface roughness elements in Antarc-
tica is a kind of snow ridge known as sastrugi. These are
meter-scale erosional features aligned with the prevailing
wind that formed them. The formation of sastrugi may be
responsible for an increase in the sastrugi drag coefficient
(form drag), leading to an increase in surface roughness and
hence to a loss of kinetic energy available for erosion. This is
negative feedback for the aeolian transport of snow, as an
increase in the roughness length reduces wind speed. An-
dreas (1995) estimated the timescale for sastrugi formation
to be half a day. Sastrugi can be buried if precipitation oc-
curs, thereby reducing surface roughness. All these effects
are taken into account in the improved version of the snow-
pack sub-model concerning the parameterization of z0R (see
Gallée et al., 2013). Finally, the modeled roughness length
results from a combination of z0S and z0R. MAR also ac-
counts for the influence of orographic roughness (Jourdain
The Cryosphere, 9, 1373–1383, 2015 www.the-cryosphere.net/9/1373/2015/
C. Amory et al.: Observed and simulated aeolian snow mass fluxes 1377
and Gallée, 2010), but its contribution to the computation of
the roughness length was neglected here, as our study is re-
stricted to the coastal slopes of Adélie Land (Fig. 1).
Once aeolian transport begins, the concentration of snow
particles in the saltation layer (ηs), expressed in kilograms
of particles per kilograms of air, is parameterized from
Pomeroy (1989):
ηS =
0 if u∗R < u∗tR
esalt
(u2∗R−u
2∗tR
ghsalt
)if u∗R ≥ u∗tR,
(5)
where u∗R is the friction velocity for a rough surface in
m s−1, esalt is the saltation efficiency equal to 3.25, g is the
gravitational acceleration in m s−2, and hsalt is the saltation
height in m, a function of u∗R (Pomeroy and Male, 1992).
As in Gallée et al. (2013), densification of the snow-
pack by the wind is included in SISVAT from the work of
Kotlyakov (1961), i.e., the density of deposited blown snow
particles is parameterized as a function of the wind speed at
10 m a.g.l. (U10):
ρ = 104(U10− 6)1/2, (6)
where ρ is the snow density in kg m−3 and U10 > 6 m s−1.
In turn, an increase in the density of the surface snowpack is
responsible for an increase in the threshold friction velocity
for erosion. This is negative feedback.
3.2 Model configuration
MAR was run over Adélie Land for the whole month of Jan-
uary 2011. The modeling grid and setup were the same as
those described in Gallée et al. (2013): the integrative do-
main covers an area of about 450 km× 450 km with a 5 km
horizontal resolution (Fig. 1). This domain was chosen so as
to include the katabatic wind system that develops over the
slopes of Adélie Land starting at the break in slope roughly
250 km inland. Since the size of the domain does not signif-
icantly influence simulated wind speed (Gallée et al., 2013),
we chose a small domain to limit numerical costs. Lateral
forcing and sea-surface conditions were taken from ERA-
Interim. Sixty vertical levels were used to simulate the at-
mosphere, with a first level 2 m in height and a vertical reso-
lution of 2 m in the 12 lowest levels. A spin-up, as described
in Gallée et al. (2013), was applied so as to achieve relative
equilibrium between the snowpack and the atmospheric con-
ditions. The simulation started on 1 December 2010, that is,
1 month before the period in which we were interested.
Erosion of snow by the wind is a highly nonlinear pro-
cess. Therefore, a good simulation of the atmospheric flow
that drives aeolian snow transport events is a prerequisite
to simulate the timing of their occurrence for the right rea-
sons. In the model, the roughness length partly depends on
wind speed, whose vertical evolution is in turn controlled by
the roughness length in a feedback fashion. As in Gallée et
al. (2013), z0 was calibrated to correctly reproduce the wind
minima measured at D17.
4 Comparison of field data and model outputs
The aim of this section is to provide a detailed comparison
between observed and modeled meteorological variables in-
cluding aeolian snow mass fluxes. The model performances
are assessed using the efficiency statistical test (E) proposed
by Nash and Sutcliffe (1970):
E = 1− (RMSE/s)2, (7)
where s is the standard deviation of the observations and
RMSE is the root mean squared error of the simulated vari-
able. An efficiency index of 1 means a perfect simulation
(RMSE= 0) and a value of 0 or less means that the model
is no better than a minimalist model whose output constantly
equals the mean value of the modeled variable over the time
period concerned. Wind speed and relative humidity were
compared at a height of 2 m above the surface. Simulation
data were extracted from the nearest grid point to the AWSS
concerned. Simulated snow mass fluxes were first obtained at
the coarse resolution (2 m) of the 3-D model. To account for
the marked decrease in aeolian snow mass fluxes within the
first 2 m, a dimensionless correction factor (A) was applied.
This factor results from comparing the snow mass fluxes
computed in our 3-D MAR simulation and those obtained
with a 1-D version of the MAR model using the same param-
eterization and a higher vertical resolution with five levels
describing the first meter above the surface. Corrected snow
mass fluxes are calculated as
µlC = µlRA, (8)
where µIC is the corrected flux for the lowest layer (0–2 m)
and µIR the raw flux from MAR for the lowest layer, both
in g m−2 s−1. µIC is compared with the mean observed snow
mass flux from 0 to 2 m a.g.l. (µ0−2 m), which is calculated
as
µ0−2 m =µ1h1+µ2h2
h1+h2
, (9)
where µi is the observed snow mass flux integrated over the
emerged length hi of the corresponding 2G-FlowCapt™ sen-
sor, in g m−2 s−1 and m, respectively.
The comparison first focused on wind speed, which is the
driving force behind aeolian snow transport. The timing of
aeolian snow transport events was then studied, together with
an evaluation of both friction and threshold friction velocities
for a period with no concomitant precipitation at site D17.
The aeolian snow mass fluxes were then analyzed at D47.
We also paid attention to relative humidity so as to evaluate
the sublimation of windborne snow particles, since it plays an
important role in the ASMB (Lenaerts et al., 2012a). Model
sensitivity to roughness length is analyzed in Sect. 4.4.
www.the-cryosphere.net/9/1373/2015/ The Cryosphere, 9, 1373–1383, 2015
1378 C. Amory et al.: Observed and simulated aeolian snow mass fluxes
Figure 3. Top: observed (black) and simulated (red) wind speed at
a height of 2 m. Bottom: aeolian snow transport events: comparison
of observed snow mass fluxes from 0 to 1 m (black) and simulated
fluxes from 0 to 2 m (red) at the D17 site (bottom left) and at the
D47 site (bottom right). Observed snow mass fluxes from 1 to 2 m
(blue) are also given for the D47 site.
4.1 Wind speed
Wind speed was correctly simulated by the model (Fig. 3)
with an efficiency of 0.60 and 0.37 for D17 and D47, respec-
tively. Variations were correctly represented but wind speeds
above 10 m s−1 were underestimated, particularly at site D47
where the model consistently underestimated wind speed by
about 2 m s−1. The high efficiency for wind speed at D17
suggests that z0 might be correctly modeled, while the lower
efficiency and the systematic negative bias at D47 strongly
suggest overestimation of z0 at this grid point.
MAR simulated a median z0 value of 3.2 mm at D17 for
our period of interest. This variable could only be compared
to observations at D17 since its determination using the pro-
file method (Garrat, 1992) using Eq. (1) requires measure-
ment of wind speed at several levels. During January 2011,
atmospheric stratification was mostly near-neutral at D17
owing to mixing caused by katabatic winds. The roughness
length z0 was computed by fitting Eq. (1) with the observed
profiles using least-square techniques with the four upper
cup anemometers (the two lowest cup anemometers were not
functioning correctly). The instruments’ elevations above the
surface were measured manually at the beginning of Jan-
uary 2011, but variations caused by accumulation/ablation
processes during the remainder of the month of January are
not known. Errors in measurement heights would introduce
a curvature to the modeled wind profile given by Eq. (1) that
could produce erroneous values of z0. To reduce z0 uncer-
tainty resulting from this discrepancy, we only considered
cases where linear fits were providing determination coeffi-
cients above 0.98. This threshold allows removing vertical
profiles when wind speed was diverging from logarithmic
profiles. The median value of the resulting z0 was 2.3 mm
for the entire month of study, lower but still close to the one
simulated by MAR.
This comparison suggests a possible overestimation of z0
by MAR. Nevertheless, this overestimation is not sufficient
to explain the tendency of the model to miss wind maxima.
This behavior may also be due to the E-ε turbulent scheme,
which is based on the small eddies concept. During strong
winds, turbulent eddies have a large vertical extent and are
responsible for the deflection of higher air parcels, which
represent a source of momentum that can be transported to
the surface in gusts. The E-ε turbulence scheme cannot re-
produce these large eddies or the gusts associated with strong
wind events. The use of a non-local turbulence scheme would
possibly improve this aspect of the simulation.
Finally, at D47, the original configuration of Gallée et
al. (2013) resulted in a median z0 value of approximately
3.4 mm for the simulated period. Although somewhat higher,
this value is consistent with other millimetric z0 values used
in realistic simulations of the Antarctic surface wind field
(Reijmer et al., 2004; Lenaerts et al., 2012b). However, the
model behaved differently with respect to wind speed de-
pending on the location (Fig. 3). Consequently, a single cal-
ibration of z0 would not represent wind speed with the same
accuracy at the two locations.
4.2 Occurrence of aeolian snow transport events
First we compare the observed and simulated aeolian snow
transport events in terms of occurrence. The timing of events
at D17 and D47 detected by the 2G-FlowCapt™ sensor mea-
suring snow particle impacts in the first meter above the sur-
face was correctly simulated by the model except between
12 January and 19 January (Fig. 3). For this period, the
field reports mentioned that drifting snow at D17 was lim-
ited to less than 1 m above the surface. The same observa-
tion was made at D47 as the 2G-FlowCapt™ installed from
1 to 2 m above the surface measured negligible snow mass
fluxes (Fig. 3). Indeed, MAR failed to reproduce aeolian
snow transport events when the maximum particle height was
less than 1 m above the surface (Fig. 3). The coarse vertical
resolution of the first layers of the MAR (2 m) may explain
part of this discrepancy, but corrections of fluxes made with
the Eq. (9) should partly account for this aspect. The preven-
tion of erosion in the model may, thus, be related to processes
involving snowpack properties and/or friction conditions at
the surface. This assumption can be investigated by analyz-
ing both modeled friction and threshold friction velocities.
Like for z0, friction and threshold friction velocities were
only compared with observations at D17 using the same de-
termination procedure. The 95 % confidence limit of each
u∗ was calculated to account for statistical errors associated
with the logarithmic profile (Wilkinson, 1984). The lowest
2G-FlowCapt™ was in contact with the ground and allowed
the detection of aeolian snow transport events: u∗t was com-
puted as the u∗ value as soon as the observed flux value ex-
ceeded 0.001 g m−2 s−1. This calculation is only valid with-
out snowfall occurrence. Indeed, when snow falls during
The Cryosphere, 9, 1373–1383, 2015 www.the-cryosphere.net/9/1373/2015/
C. Amory et al.: Observed and simulated aeolian snow mass fluxes 1379
Figure 4. Top panel: comparison between observed aeolian snow
mass fluxes from 0 to 1 m (black), simulated fluxes from 0 to 2 m
(red), and precipitation from ERA-Interim at D17. The black frame
identifies the period without precipitation analyzed in the bottom
panel. Bottom panel: comparison of observed/simulated friction
velocity (black line/red line, respectively) and observed/simulated
threshold friction velocity (dashed line/black circles, respectively)
at D17 for a transport period with no precipitation. The horizon-
tal green bars represent the observed aeolian snow transport events
numbered from 1 to 6.
windy conditions, the sensor detects the presence of airborne
snow particles but does not distinguish between precipitat-
ing snowflakes and snow grains that were eroded from the
surface by the wind. Accounting for situation with snow-
fall occurrence would introduce a bias in the u∗t values
since the detection of an aeolian snow transport event by the
2G-FlowCapt™ is not necessarily associated with erosion of
snow. Therefore, for an accurate evaluation of u∗t, snowfall
events need to be removed from the data. For this purpose, we
used the ERA-Interim reanalysis from the European Center
for Medium-range Weather Forecast, which appears to be the
most appropriate support for estimating precipitation rates
in the study area (Palerme et al., 2014). According to the
ERA-Interim data, the longest period without precipitation
was between 12 January and 19 January. During this period,
six transport events were identified and six threshold fric-
tion velocities were determined (Fig. 4) from observations.
Nevertheless, MAR did not simulate any aeolian snow trans-
port event during the entire period. As shown in Fig. 4, the
simulated u∗ is lower than the observed one, while the sim-
ulated u∗t is overestimated and higher than the simulated u∗.
This results in the absence of drifting snow in the simulation
of this period. Note the decrease in the simulated u∗t in re-
sponse to the light snowfall that occurred around 12 January
(Fig. 4).
Except for cases of drifting snow presented in the pre-
vious paragraph, the 2G-FlowCapt™ sensors recorded four
aeolian snow transport events, which, this time, were simu-
lated by the MAR. Model behavior can be assessed by com-
paring the relation between aeolian snow mass fluxes ver-
sus wind speed for the four strongest events that occurred in
Figure 5. Observed (diamonds) and simulated (red squares) snow
mass fluxes from 0 to 2 m versus the observed (and simulated re-
spectively) wind speed at 2 m in January 2011 for the four strong
aeolian snow transport events recorded at D47. Event 1 lasted from
7–10, event 2 from 21–22, event 3 from 24–26, and event 4 from
27–29 January. For the first event, the observed snow mass fluxes
are decomposed in time between a first (blue), an intermediate (pur-
ple), and a final relationship (green).
January 2011. It is well known that, at a given height, for
a given set of snow particles (i.e., a constant threshold fric-
tion velocity value), the amount of snow being transported
by the wind can be approximated by a power law of the wind
speed (Radok, 1977; Mann et al., 2000). This is clearly de-
picted in Fig. 5 for events nos. 2, 3, and 4. However, obser-
vations show that the occurrence of precipitation may im-
pact this basic relationship, and may explain part of the dif-
ference between model and measurements here (see events
nos. 2 and 4)(Naaim-Bouvet et al., 2014). Indeed, unlike the
others, the first event was characterized by a hysteresis ef-
fect (Fig. 5, upper left panel). A similar case was reported
by Gordon et al. (2010), who linked this phenomenon to the
occurrence of snowfall. This may be justified assuming a 3-
stage process of the snow mass flux–wind speed relationship
according to changes in u∗t over time: (1) the first stage de-
scribes the initiation of the blowing snow event associated
with the onset of strong winds: the aeolian snow mass flux
increases with wind speed according to the theoretical power
law described by Radok (1977), which suggests that u∗t stays
roughly unchanged; (2) the second stage is characterized by
the relative constancy of the wind speed around 17–18 m s−1,
while the aeolian snow mass flux decreases gradually, prob-
ably in response to a progressive increase in u∗t (caused by
the exhaustion of easily erodible snow or the exposure of a
harder layer); (3) finally, ERA-Interim estimates predict the
occurrence of substantial precipitation amounts leading the
same wind speed to be associated with higher aeolian snow
mass fluxes than during the two previous stages; precipitat-
ing snow particles and subsequently loosened snow particles
are added to the previous set of airborne particles which orig-
inate from the surface, and are responsible for a considerable
decrease in u∗t below the value estimated in the first stage.
www.the-cryosphere.net/9/1373/2015/ The Cryosphere, 9, 1373–1383, 2015
1380 C. Amory et al.: Observed and simulated aeolian snow mass fluxes
Figure 6. Top: observed (green) and simulated (red) snow mass
fluxes from 0 to 2 m. Bottom: observed (black) and simulated (red)
relative humidity 2 m above the surface.
Then, as the wind weakens, the snow mass flux decreases to
negligible values, and the event ends.
Despite the good quality of ERA-Interim precipitation
data, we suspect that both modeled occurrences and amounts
may differ from observations. The modeled u∗t and hori-
zontal snow transport include biases caused by inaccurately
modeled occurrences, which may partly justify that modeled
amounts of blowing snow do not exactly fit with a perfect
power law of wind speed. Given the previous analysis, the
snow mass flux–wind speed relationship is well represented
by MAR, suggesting that the model correctly reproduced the
underlying processes. The influence of snowfall is also evi-
denced by the model outputs, showing that the largest sim-
ulated snow mass fluxes (∼ 90–100 g m−2 s−1) occur at a
wind speed of around 13 m s−1, although the model simu-
lates stronger wind speeds. The second and fourth events
(Fig. 5, right panels) are particularly affected. This reflects
the decrease in u∗t associated with the heavy snowfall events
simulated at that time.
4.3 Aeolian snow mass fluxes
Next, we compare the measured aeolian snow mass fluxes
and relative humidity with the model outputs in Fig. 6. The
evaluation is based only on the AWSS at D47, since this sta-
tion, unlike D17, provides information on the snow mass
fluxes from 0 to 2 m a.g.l., allowing a comparison with the
first level of the model. As mentioned above, MAR only sim-
ulated aeolian snow transport events at D47 when the max-
imum particle height was above 1 m. Even in these cases,
MAR consistently underestimated the aeolian snow mass
fluxes measured by the 2G-FlowCapt™. The modeled under-
estimation is even higher knowing that the 2G-FlowCapt™
sensor already underestimates actual snow mass flux (Trou-
villiez et al., 2015). An important negative bias between ob-
served and simulated relative humidity appeared, even when
the model correctly simulated the timing of the aeolian snow
Table 2. Comparison of Nash tests for wind speed, aeolian snow
mass flux, and relative humidity at D47 for various median values
of z0.
Calibrated z0 Wind Snow mass Relative
(median value, mm) speed flux humidity
3 0.37 −0.06 −4.77
0.5 0.8 0.2 −0.14
0.2 0.86 0.26 −0.01
0.1 0.89 0.32 0.16
transport events (Fig. 6). This underestimation may result
from the underestimation of the sublimation of the blown
snow particles, linked to the underestimation of the concen-
tration of blown snow particles in the lower model layer.
Overall, simulated aeolian snow mass fluxes were twice
lower than those provided by the 2G-FlowCapt™ sensors for
equal wind speed values except during snowfall events. The
model also failed to reproduce strong aeolian snow trans-
port events with wind speeds above 13 m s−1 and snow mass
fluxes in excess of 100 g m−2 s−1. As a result, the simu-
lated horizontal snow transport through the first 2 m a.g.l.
at D47 in January 2011 was underestimated by roughly
a factor of 10 compared to observations; the model cal-
culated 5768 kg m−2, while the 2G-FlowCapt™ measured
67 509 kg m−2.
4.4 Model sensitivity to roughness length for
momentum
Since wind speed is the most important force behind snow
erosion (Gallée et al., 2013), we performed a sensitivity test
to see whether lower z0 was giving more accurate modeled
wind speed values. We tuned the model with different z0 val-
ues to assess wind speed relationship with z0. According to
theory, the higher the wind speed, the higher the snow mass
fluxes. As a consequence, larger relative humidity was mod-
eled close to the surface with lower z0. This resulted from
sublimation of additional windborne snow particles in the
lowest levels of the model. The model evaluation was per-
formed with wind speed values measured at D47 over the
entire study period. Results for various median z0 values are
summarized in Table 2. The best results were obtained for a
reduction of z0 by a factor of 30 (i.e., a median z0 value of
0.1 mm) over the simulated period at D47. The correspond-
ing statistical efficiency for wind speed reached 0.89, while
the efficiencies of the snow mass flux and relative humid-
ity both became positive. The resulting local snow transport
was still underestimated but only by about one third of the
observed value. Nevertheless, reducing z0 did not enable the
reproduction of the small drifting snow events that occurred
between 12 January and 19 January, suggesting that part of
the processes leading to surface state evolution is not fully re-
The Cryosphere, 9, 1373–1383, 2015 www.the-cryosphere.net/9/1373/2015/
C. Amory et al.: Observed and simulated aeolian snow mass fluxes 1381
produced by the MAR. Therefore, further improvements are
still necessary.
5 Discussion
The original calibration of z0 (Gallée et al., 2013) produced
satisfactory results for modeled wind speed at D17, but the
same good behavior was not reproduced at D47, another
measurement point located 100 km away. We showed that
a 1-order decrease in the magnitude of z0 significantly im-
proved the simulation quality at D47, but we cannot affirm
that this modification gives a more relevant z0 for this site.
In other words, obtaining a better representation of the eval-
uated variables does not mean that modeled z0 agreed with
observed z0 or that the processes governing its behavior were
correctly modeled. This may be the result of error compen-
sations.
Nevertheless, this suggests that z0 may vary regionally. In
particular, D17 and D47 are located on either side of the dry-
snow line, and the temperature regime at the two locations
is sufficiently contrasted to explain differences in snowpack
properties such as internal cohesion, density or aerodynamic
resistance, which are involved in different types of feedback
between z0 and snow transport by the wind. In this case, dis-
tributed modeling should account for spatial variations of z0
to allow a consistent representation of the aeolian snow mass
fluxes. Smeets and van den Broeke (2008) showed that z0
can vary from 2 to 3 orders of magnitude during the abla-
tion season between coastal and inland locations situated on
either side of the equilibrium line of West Greenland. Conse-
quences on wind speed and aeolian snow mass fluxes would
be important, as demonstrated at D17, where the agreement
between modeled and observed wind speed was significantly
reduced assuming a lower z0 value. Indeed, the modeled
wind speed bias increased from −1 to +1.5 m s−1 for the
entire simulated period when z0 was changed from 3.2 to
0.1 mm. Further investigations of z0 and its linkages with
snow transport by the wind in Adélie Land are thus required.
Using the original calibration, the simulated horizontal
snow transport in the first 2 m above the surface at site D47
in January 2011 was about 10 times lower than the observed
value. This difference could mainly be explained by overesti-
mation of the modeled z0 and subsequent underestimation of
the wind speed. The drag partition dictating the form drag in
the MAR is currently parameterized with a qualitative formu-
lation (Gallée et al., 2013) adapted from the work of Andreas
and Claffey (1995) on sea ice in the Weddell Sea. Validity of
this formulation should be reassessed given the differences
in surface drag properties between coastal margins of Adélie
Land and sea ice. Indeed, the severe katabatic wind regime
characterizing the slopes of Adélie Land may promote aero-
dynamical adjustment of the snow surface. Thus, the form
drag is likely lower than for sea ice, which experiences much
lower wind speeds. In particular, overestimation of z0 in the
simulation resulted in a deficit of shear stress available for
snow erosion, thus leading to underestimation of the mod-
eled snow mass fluxes. As form drag is the main contribu-
tor to surface transfer of momentum (Jackson and Carroll,
1978; Andreas, 1995; Smeets and van den Broeke, 2008)
over rough snow/ice fields, a more sophisticated represen-
tation of z0 that accounts for potential spatial and temporal
variations in the form drag in the model is needed.
6 Conclusions
The regional climate model MAR, which includes a coupled
snowpack/aeolian snow transport parameterization, was run
at a fine spatial resolution (5 km horizontally and 2 m ver-
tically) for a period of 1 summer month in coastal Adélie
Land, East Antarctica. The study reported here is a step for-
ward in the model evaluation of the aeolian transport of snow.
The study by Gallée et al. (2013) focused on checking that
the MAR was able to reproduce drifting snow occurrences
in January 2010 at one near-coastline location (D3, ∼ 5 km
from the coast) in Adélie Land. In this paper, using the same
model setup, we present a quantitative evaluation of the aeo-
lian erosion process in the same region, by comparing model
outputs with (1) observed aeolian snow mass fluxes and rel-
ative humidity at D47 (∼ 100 km from the coast) in Jan-
uary 2011, and (2) observed friction velocity and threshold
friction velocity for snow transport over a 7-day period with-
out precipitation in January 2011 at D17 (located ∼ 10 km
from the coast). This comparison highlighted the model qual-
ities and discrepancies. Firstly, wind speed variations were
accurately represented by the MAR although the model un-
derestimated the wind maxima at D17 and more generally
the wind speed at D47. This underestimation may be justi-
fied by an incomplete representation of z0 and by the use of
a turbulent scheme based on the small eddies concept. Sec-
ondly, the occurrence of the aeolian snow transport events
was well reproduced except for events when the maximum
particle height was less than 1 m above the surface. This
probably results from a combination of underestimation of
the friction velocity, overestimation of the threshold friction
velocity and the too-coarse vertical resolution (2 m) of the
MAR near the surface. Thirdly, at the same wind speed, mod-
eled snow mass fluxes were twice lower than those measured
by the 2G-FlowCapt™ sensor, while it is known that this
sensor already underestimates the snow mass fluxes of ae-
olian snow transport. Finally, the model underestimated the
large snow mass fluxes (> 100 g m−2 s−1) and the associated
strong winds (> 13 m s−1). Comparison with measurements
from 2G-FlowCapt™ sensors at D47 revealed that the model
underestimates the horizontal snow transport over the first
2 m above the ground by a factor of 10. Our results show that
using the original setup of Gallée et al. (2013), MAR would
significantly underestimate the contribution of aeolian snow
transport to the ASMB. For that reason, new observations
www.the-cryosphere.net/9/1373/2015/ The Cryosphere, 9, 1373–1383, 2015
1382 C. Amory et al.: Observed and simulated aeolian snow mass fluxes
are currently underway to better assess the contribution of
the form drag to z0 in coastal Adélie Land and to develop a
more robust calibration process for z0.
Acknowledgements. This comparison would not have been possi-
ble without the financial support of the European program FP-7
ICE2SEA, grant no. 226375, and the financial and logistical sup-
port of the French Polar Institute IPEV (program CALVA-1013).
Additional support by INSU through the LEFE/CLAPA project
and OSUG through the CENACLAM/GLACIOCLIM observa-
tory is also acknowledged. The MAR simulations were run on
CNRS/IDRIS and Université Joseph Fourier CIMENT computers.
We would like to thank all the on-site personnel in Dumont
d’Urville and Cap Prud’homme for providing precious help in
the field, and the two anonymous reviewers for their constructive
remarks that helped to improve the manuscript considerably.
Edited by: P. Marsh
References
Agosta, C., Favier, V., Genthon, C., Gallée, H., Krinner, G.,
Lenaerts, J. T. M., and van den Broeke, M. R.: A 40-year
accumulation dataset for Adélie Land, Antarctica and its ap-
plication for model validation, Clim. Dynam., 38, 75–86,
doi:10.1007/s00382-011-1103-4, 2012.
Andreas, E. L.: Air-ice drag coefficients in the western weddell sea.
2. A model based on form drag and drifting snow, J. Geophys.
Res., 100, 4833–4843, 1995.
Andreas, E. L. and Claffey K. J.:. Air-ice drag coefficients in the
western weddell sea. 1. Values deduced from profile measure-
ments, J. Geophys. Res., 100, 4821–4831, 1995.
Bintanja, R.: Snowdrift suspension and atmospheric turbulence.
Part I: Theoretical background and model description, Bound.-
Layer Meteorol., 95, 343–368, 2000.
Budd, W. F.: The drifting of nonuniform snow particles, in: Studies
in Antarctic Meteorology, Vol. 9, 59–70, AGU, Washington DC,
1966.
Budd, W. F., Dingle, W. R. J., and Radok, U.: The Byrd snow drift
project outline and basic results, in Studies in Antarctic Meteo-
rology, Vol. 9, edited by: Rubin, M. J., 71–134, American Geo-
physical Union, Washington DC, 1966.
Cierco, F.-X., Naaim-Bouvet, F., and Bellot, H.: Acoustic sen-
sors for snowdrift measurements: How should they be used
for research purposes?, Cold Reg. Sci. Technol., 49, 74–87,
doi:10.1016/j.coldregions.2007.01.002, 2007.
Das, I., Bell, R. E., Scambos, T. A., Wolovick, M., Creyts, T. T.,
Studinger, M., Frearson, N., Nicolas, J. P., Lenaerts, J. T. M., and
van den Broeke, M. R.: Influence of persistent wind scour on the
surface mass balance of Antarctica, Nat. Geosci., 6, 367–371,
2013.
De Ridder, K. and Gallée, H.: Land Surface–Induced
Regional Climate Change in Southern Israel, J.
Appl. Meteorol., 37, 1470–1485, doi:10.1175/1520-
0450(1998)037<1470:LSIRCC>2.0.CO;2, 1998.
Déry, S. J. and Yau, M. K.: Large-scale mass balance effects of
blowing snow and surface sublimation, J. Geophys. Res., 107,
4679, doi:10.1029/2001JD001251, 2002.
Duynkerke, P. G.: Application of the E – ε Turbulence Closure
Model to the Neutral and Stable Atmospheric Boundary Layer,
J. Atmos. Sci., 45, 865–880, 1988.
Favier, V., Agosta, C., Genthon, C., Arnaud, L., Trouvillez, A.,
and Gallée, H.: Modeling the mass and surface heat budgets in
a coastal blue ice area of Adélie Land, Antarctica, J. Geophys.
Res., 116, F03017, doi:10.1029/2010JF001939, 2011.
Fettweis, X., Gallée, H., Lefebre, F., and Van Ypersele, J.: Green-
land Surface Mass Balance simulated by a Regional Climate
Model and Comparison with satellite derived data in 1990–1991,
Clim. Dynam., 24, 623—640, doi:10.1007/s00382-005-0010-y,
2005.
Frezzotti, M., Gandolfi, S., and Urbini, S.: Snow megadunes in
Antarctica: Sedimentary structure and genesis, J. Geophys. Res.,
107, 4344, doi:10.1029/2001JD000673, 2002.
Frezzotti, M., Pourchet, M., Flora, O., Gandolfi, S., Gay, M., Urbini,
S., Vincent, C., Becagli, S., Gragnani, R., Proposito, M., Severi,
M. T. R., Udisti, R., and Fily, M.: New Estimations of Precip-
itation and Surface Sublimation in East Antarctica from Snow
Accumulation Measurements, Cim. Dynam., 23, 803–813, 2004.
Gallée, H.: Simulation of the Mesocyclonic Activity in the Ross
Sea, Antarctica, Mon. Weather Rev., 123, 2051–2069, 1995.
Gallée, H.: A simulation of blowing snow over the Antarctic ice
sheet, Ann. Glaciol., 26, 203–205, 1998.
Gallée, H. and Gorodetskaya, I. V.: Validation of a limited area
model over Dome C, Antarctic Plateau, during winter, Clim. Dy-
nam., 34, 61–72, doi:10.1007/s00382-008-0499-y, 2010.
Gallée, H. and Schayes, G.: Development of a three-dimensional
meso-γ primitive equation model: katabatic winds simulation in
the area of Terra Nova Bay, Antarctica, Mon. Weather Rev., 122,
671–685, 1994.
Gallée, H., Guyomarc’h, G., and Brun, É.: Impact of snow drift on
the antarctic ice sheet surface mass balance: possible sensitivity
to snow-surface properties, Bound.-Layer Meteorol., 99, 1–19,
2001.
Gallée, H., Peyaud, V., and Goodwin, I.: Simulation of the net snow
accumulation along the Wilkes Land transect, Antarctica, with a
regional climate model, Ann. Glaciol., 41(January), 1–6, 2005.
Gallée, H., Trouvilliez, A., Agosta, C., Genthon, C., Favier, V., and
Naaim-Bouvet, F.: Transport of Snow by the Wind: A Compari-
son Between Observations in Adélie Land, Antarctica, and Sim-
ulations Made with the Regional Climate Model MAR, Bound.-
Layer Meteorol., 146, 133–147, doi:10.1007/s10546-012-9764-
z, 2013.
Garcia, R.: Mesures de transport de neige par le vent à la station
Charcot, La météorologie, 57, 205–213, 1960.
Garratt, J. R.: The Atmospheric Boundary Layer, Cambridge Uni-
versity Press, Cambridge, 1992.
Genthon, C., Lardeux, P., and Krinner, G.: The surface accumula-
tion and ablation of a coastal blue-ice area near Cap Prudhomme,
Terre Adélie, Antarctica, J. Glaciol., 53, 635–645, 2007.
Gordon, M., Biswas, S., Taylor, P. A., Hanesiak, J., Albarran-
Melzer, M., and Fargey, S.: Measurements of drifting and blow-
ing snow at Iqaluit, Nunavut, Canada during the star project,
Atmos.-Ocean, 48, 81–100, doi:10.3137/AO1105.2010, 2010.
Guyomarc’h, G. and Mérindol, L.: Validation of an application for
forecasting blowing snow, Ann. Glaciol., 26, 138–143, 1998.
The Cryosphere, 9, 1373–1383, 2015 www.the-cryosphere.net/9/1373/2015/
C. Amory et al.: Observed and simulated aeolian snow mass fluxes 1383
Jackson, B. S. and Carroll, J. J.: Aerodynamic roughness as a
function of wind direction over asymmetric surface elements,
Bound.-Layer Meteorol., 14, 323–330, 1978.
Jourdain, N. C. and Gallée, H.: Influence of the orographic rough-
ness of glacier valleys across the Transantarctic Mountains in
an atmospheric regional model, Clim. Dynam., 36, 1067–1081,
doi:10.1007/s00382-010-0757-7, 2010.
Kodama, Y., Wendler, G., and Gosink, J.: The effect of blowing
snow on katabatic winds in Antarctica, Ann. Glaciol., 6, 59–62,
1985.
Kotlyakov, V. M.: Results of a Study of the Processes of Forma-
tion and Structure of the Upper Layer of the Ice Sheet in East-
ern Antarctica, in: Symposium on Antarctic Glaciology, 88–99,
IAHS Publication, Helsinki, 1961.
Lefebre, F., Fettweis, X., Gallée, H., Van Ypersele, J., Marbaix,
P., Greuell, W., and Calanca, P.: Evaluation of a high-resolution
regional climate simulation over Greenland, Clim. Dynam., 25,
99–116, doi:10.1007/s00382-005-0005-8, 2005.
Lenaerts, J. T. M., van den Broeke, M. R., Déry, S. J., König-
Langlo, G., Ettema, J., and Munneke, P. K.: Modelling snowdrift
sublimation on an Antarctic ice shelf, The Cryosphere, 4, 179–
190, doi:10.5194/tc-4-179-2010, 2010.
Lenaerts, J. T. M., van den Broeke, M. R., van de Berg, W. J., van
Meijgaard, E., and Kuipers Munneke, P.: A new, high-resolution
surface mass balance map of Antarctica (1979–2010) based on
regional atmospheric climate modeling, Geophys. Res. Lett., 39,
L0451, doi:10.1029/2011GL050713, 2012a.
Lenaerts, J. T. M., van den Broeke, M. R., Déry, S. J., van Mei-
jgaard, E., van de Berg, W. J., Palm, S. P., and Sanz Rodrigo,
J.: Modeling drifting snow in Antarctica with a regional climate
model: 1. Methods and model evaluation, J. Geophys. Res., 117,
D05108, doi:10.1029/2011JD016145, 2012b.
Lenaerts, J. T. M., van den Broeke, M. R., Scarchilli, C., and Agosta,
C.: Impact of model resolution on simulated wind, drifting snow
and surface mass balance in Terre Adélie, East Antarctica, J.
Glaciol., 58, 821–829, doi:10.3189/2012JoG12J020, 2012c.
Leonard, K. C., Tremblay, L.-B., Thom, J. E., and MacAyeal, D. R.:
Drifting snow threshold measurements near McMurdo station,
Antarctica: A sensor comparison study, Cold Reg. Sci. Technol.,
70, 71–80, doi:10.1016/j.coldregions.2011.08.001, 2011.
Lorius, C.: Contribution to the knowledge of the Antarctic ice sheet:
a synthesis of glaciological measurements in Terre Adélie, J.
Glaciol., 4, 79–92, 1962.
Madigan, C. T.: Meteorology. Tabulated and reduced records of the
Cape Denison Station, Adélie Land. Australasian Antarctic Ex-
pedition 1911–1914 Scientific Reports, Serie B, Vol. 4, 1929.
Mann, G. W., Anderson, P. S., and Mobbs, S. D.: Profile measure-
ments of blowing snow at Halley, Antarctica, J. Geophys. Res.,
105, 24491–24508, doi:10.1029/2000JD900247, 2000.
Marticorena, B. and Bergametti, G.: Modeling the atmospheric dust
cycle: 1. Design of a soil-derived dust emission scheme, J. Geo-
phys. Res., 100, 16415–16430, 1995.
Naaim-Bouvet, F., Bellot, H., and Naaim, M.: Back anal-
ysis of drifting-snow measurements over an instru-
mented mountainous site, Ann. Glaciol., 51, 207–217,
doi:10.3189/172756410791386661, 2010.
Naaim-Bouvet, F., Bellot, H., Nishimura, K., Genthon, C., Palerme
C., Guyomarc’h, G., and Vionnet, V.: Detection of snow-
fall occurrence during blowing snow events using photo-
electric sensors, Cold Reg. Sci. Technol., 106–107, 11–21,
doi:10.1016/j.coldregions.2014.05.005, 2014.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through con-
ceptual models Part I – A discussion of principles, J. Hydrol., 10,
282–290, 1970.
Owen, P. R.: Saltation of uniform grains in air, J. Fluid Mech., 20,
225–242, 1964.
Palerme, C., Kay, J. E., Genthon, C., L’Ecuyer, T., Wood, N. B., and
Claud, C.: How much snow falls on the Antarctic ice sheet?, The
Cryosphere, 8, 1577–1587, doi:10.5194/tc-8-1577-2014, 2014.
Palm, S. P., Yang, Y., Spinhirne, J. D., and Marshak, A.: Satellite
remote sensing of blowing snow properties over Antarctica, J.
Geophys. Res., 116, D16123, doi:10.1029/2011JD015828, 2011.
Parish, T. R. and Wendler, G.: The katabatic wind regime at Adélie
Land, Antarctica, Int. J. Climatol., 11, 97–107, 1991.
Pomeroy, J. W.: A process-based model of snow drifting, J. Glaciol.,
13, 237–240, 1989.
Pomeroy, J. W. and Male, D. H.: Steady-state suspension of snow,
J. Hydrol., 136, 275–301, doi:10.1016/0022-1694(92)90015-N,
1992.
Prud’homme, A. and Valtat, B.: Les observations météorologiques
en Terre Adélie 1950–1952 – Analyse critique, Expéditions po-
laires françaises, Paris, 1957.
Radok, U.: Snow Drift, J. Glaciol., 19, 123–139, 1977.
Reijmer, C. H., van Meijgaard, E., and van den Broeke, M. R.: Nu-
merical studies with a regional atmospheric climate model based
on changes in the roughness length for momentum and heat over
Antarctica, Bound.-Layer Meteorol., 111, 313–337, 2004.
Scarchilli, C., Frezzotti, M., Grigioni, P., De Silvestri, L., Ag-
noletto, L., and Dolci, S.: Extraordinary blowing snow trans-
port events in East Antarctica, Clim. Dynam., 34, 1195–1206,
doi:10.1007/s00382-009-0601-0, 2010.
Smeets, C. J. P. P. and van den Broeke, M. R.: Temporal and spatial
variations of the aerodynamic roughness length in the ablation
zone of the Greenland Ice Sheet, Bound.-Layer Meteorol., 128,
315–338, 2008.
Trouvilliez, A., Naaim-Bouvet, F., Genthon, C., Piard, L., Favier,
V., Bellot, H., Agosta, C., Palerme, C., Amory, C., and Gal-
lée, H.: A novel experimental study of aeolian snow transport
in Adélie Land (Antarctica), Cold Reg. Sci. Technol., 108, 125–
138, doi:10.1016/j.coldregions.2014.09.005, 2014.
Trouvilliez, A., Naaim-Bouvet, F., Bellot, H., Genthon, C., and Gal-
lée, H.: Evaluation of Flowcapt acoustic sensor for snowdrift
measurements, J. Atmos. Ocean. Tech., in press, 2015.
Wamser, C. and Lykossov, V. N.: On the friction velocity during
blowing snow, Contrib. to Atmos. Phys., 68, 85–94, 1995.
Wendler, G.: On the blowing snow in Adélie Land, eastern Antarc-
tica. A contribution to I.A.G.O, in: Glacier fluctuations and cli-
matic change, edited by: Oerlemans, J., 261–279, Reidel, Dor-
drecht, 1989.
Wendler, G., Stearns, C. R., Dargaud, G., and Parish, T. R.: On the
extraordinary katabatic winds of Adélie Land, J. Geophys. Res.,
102, 4463–4474, 1997.
Wilkinson, R. H.: A Method for Evaluating Statistical Errors Asso-
ciated with Logarithmic Velocity Profiles, Geo-Marine Lett., 3,
49–52, 1984.
Xiao, J., Bintanja, R., Déry, S. J., Mann, G. W., and Taylor, P. A.: An
intercomparison among four models of blowing snow, Bound.
Layer Meteorol., 97, 109–135, 2000.
www.the-cryosphere.net/9/1373/2015/ The Cryosphere, 9, 1373–1383, 2015
Recommended